A little paragraph from one real dataset, with a few little changes to protect students' private information. Permissions are given.

Goals

You are going to help teachers with only the data: 1. Prediction: To tell what makes a brilliant student who can apply for a graduate school, whether abroad or not. 2. Application: To help those who fails to apply for a graduate school with advice in job searching.

Tips

1. Educational data may have subtle structures, hierarchies and heterogeneity are probably involved. Simple regressions can hardly make any difference. Also, you should keep an eye on the collinearity in some indicators collected by teachers who have already forgot statistics.
2. Not all students are free to choose to apply for a graduate school, but some were born with privileges.
3. Some of the students are trying (or planning to try) to apply for a graduate school for years, you should be responsible to give advice accurately under their circumstances

About the Data

Some of the original structure are deleted or censored. For those are left: Basic data like: - ID - class: categorical, initially students were divided into 2 classes, yet teachers suspect that of different classes students may performance significant differently. - gender - race: categorical and censored - GPA: real numbers, float

Some teachers assume that scores of math curriculums can represent one's likelihood perfectly: - Algebra: real numbers, Advanced Algebra - ......

Some assume that background of students can affect their choices and likelihood significantly, which are all censored as: - from1: students' home locations - from2: a probably bad indicator for preference on mathematics - from 3: how did students apply for this university (undergraduate) - from4: a probably bad indicator for family background. 0 with more wealth, 4 with more poverty

The final indicator y: - 0, one fails to apply for the graduate school, who may apply again or search jobs in the future - 1, success, inland - 2, success, abroad

Database is provided by ASL Marketing and covers the United States of America. With ASL Marketing Reaching GenZ has never been easier. Current high school student data customized by: Class year Date of Birth Gender GPA Geo Household Income Ethnicity Hobbies College-bound Interests College Intent Email

Student data is a wide collection of contact details about students. Also, it includes personal info like name, address, and other important info. Furthermore, our student data is essential for you if you want to access it. Thus, you can use this for schools and educational institutions to keep track of entire student information. Yet, this database can make your business marketing more profitable. Similarly, it is a great way to get in touch with the right audience you target. We assure you that it can develop your specific business in a very short time. So, buy this database to get huge benefits and achieve your goals. Student number database encompasses a comprehensive collection of contact details about students. Moreover, it includes essential personal information such as names, addresses, and other crucial data points. Furthermore, access to our Student Number Database is crucial for educational institutions to effectively manage student information and streamline administrative processes. This database can also significantly enhance the profitability of your business marketing endeavors. It provides a valuable resource for identifying and connecting with your target audience. By leveraging this data, you can effectively reach out to potential customers within the student demographic, fostering growth and achieving your business objectives. At List To Data, we encourage you to acquire our database to unlock its substantial benefits and propel your business toward success.

Title: Survey of International Students

Creator: Fatema Islam Meem, Imran Hussain Mahdy

Subject: International Students; Academic and Social Integration; University of Idaho

Description: This dataset contains responses from a survey conducted to understand the factors affecting the academic success and social integration of international students at the University of Idaho.

Contributor: University of Idaho

Date: February 10, 2024

Type: Dataset

Format: CSV

Source: Goole Form

Language: English

Coverage: University of Idaho, [2014-2024]

Sources: The primary data source was a Google Form survey designed to capture international students' perspectives on their integration into the academic and social fabric of the university. Questions were developed to explore academic challenges, social integration, support systems, and overall satisfaction with their university experience.

Collection Methodology: The survey was distributed with the assistance of the International Programs Office (IPO) at the University of Idaho to ensure a broad reach among the target demographic. Efforts were made to design the survey questions to be clear, concise, and sensitive to the cultural diversity of the respondents. The collection process faced challenges, particularly in achieving a high response rate. Despite these obstacles, approximately 48 responses were obtained, providing valuable insights into the experiences of international students. The survey data were anonymized to protect respondents' privacy and maintain data integrity.

To collect feedback on their learning environment from families, students and teachers. Aids in facilitating the understanding of families perceptions, students, and teachers regarding their school. School leaders use feedback from the survey to reflect and make improvements to schools and programs. Each year all parents, teachers and students in grades 6-12 take the NYC School Survey. The survey is aligned to the DOE's Framework for Great Schools. It is designed to collect important information about each school's ability to support student success.

The data appears to be related to student order and payment information, including details like student names, order IDs, courses enrolled, payment status, and more. this data is for practice on sql queries, it is helpful for data analysis student to make visualisation on the data. data is provided by Skill course the E- Learning platform by Satish Dhawale

WWW.SKILLCOURSE.IN

The file "Indian_Students_Data.csv" contains the following information:

****Columns are :****

srno order_id student_name
payment_date course_name price payment_status payment_id email state

Since 2002, AmeriList has been the nation’s premier provider of student-marketing data, offering a broad suite of ethically compiled, highly accurate, and deliverable mailing, email, and telemarketing lists targeting families, high-school students, college-bound freshmen, enrolled college students, and adult learners for continuing education

Comprehensive Dataset Overviews • Parents of Students / Households with Children – Reach parents alongside teens and pre-teens, ideal for programs like prom services, tutoring, summer camps, and private school admissions • High-School Students – Access ~5 million U.S. students and their parents, with robust selects including GPA, class rank, SAT/GED scores, arts/athletic interests, intended college, school year, and more • College-Bound Students Database – Tap into over 3–4 million incoming freshmen making major purchases (electronics, school supplies, dorm essentials, apparel), with segmentation by college attending, GPA, sports interest, geography, income, credit usage, and more • College Students Mailing List – Access ~24.4 million enrolled college students, segmented by class year, gender, field of study, hobbies, buying habits, and more for highly targeted outreach • Adult Learners / Continuing Education – Reach over 30 million individuals who have completed some college or are interested in continuing education, vocational or trade programs

How the Data Is Compiled & Maintained AmeriList uses a rigorous, ethical data-collection methodology, aggregating information from direct responses, internet and telephone surveys, public records, club memberships, purchase history, self-reported data, and proprietary sources.

All lists undergo monthly updates and data hygiene processes, including: - CASS-certification for address standardization - DPV (Delivery Point Validation) removal of unverifiable addresses - NCOALink, LACSLink, and Address Change processing for forwarding accuracy - Do-Not-Call, DMA suppression, in-house suppression for compliance - Deceased-record scrubbing via internal and third-party checks

Recommended Uses • Parents & High-School Campaigns – Promote private schooling, test prep, student loans, scholarships, events like prom or summer camps, trade schools, teen retail, or electronics • College-Bound Freshmen – Ideal for marketing student loans, scholarships, credit cards, dorm suppliers, school supplies, electronics, study aids, and apparel • Enrolled College Students – Excellent for textbook vendors, academic supplies, coupons, food delivery, financial aid, campus services, tech products, and lifestyle brands • Adult Learners / Continuing Ed – Perfect for vocational schools, certificate programs, online learning, re-enrollment, or career enhancement marketing

With data that is fresh, accurate, and ethically sourced, AmeriList gives you the tools to launch smarter, more impactful campaigns across mail, email, and telemarketing channels. Backed by two decades of expertise, proven results, and unmatched audience coverage, AmeriList is the trusted partner for organizations that want to connect with the student market and drive measurable growth.

Verbal and Quantitative Reasoning GRE scores and percentiles were collected by querying the student database for the appropriate information. Any student records that were missing data such as GRE scores or grade point average were removed from the study before the data were analyzed. The GRE Scores of entering doctoral students from 2007-2012 were collected and analyzed. A total of 528 student records were reviewed. Ninety-six records were removed from the data because of a lack of GRE scores. Thirty-nine of these records belonged to MD/PhD applicants who were not required to take the GRE to be reviewed for admission. Fifty-seven more records were removed because they did not have an admissions committee score in the database. After 2011, the GRE’s scoring system was changed from a scale of 200-800 points per section to 130-170 points per section. As a result, 12 more records were removed because their scores were representative of the new scoring system and therefore were not able to be compared to the older scores based on raw score. After removal of these 96 records from our analyses, a total of 420 student records remained which included students that were currently enrolled, left the doctoral program without a degree, or left the doctoral program with an MS degree. To maintain consistency in the participants, we removed 100 additional records so that our analyses only considered students that had graduated with a doctoral degree. In addition, thirty-nine admissions scores were identified as outliers by statistical analysis software and removed for a final data set of 286 (see Outliers below). Outliers We used the automated ROUT method included in the PRISM software to test the data for the presence of outliers which could skew our data. The false discovery rate for outlier detection (Q) was set to 1%. After removing the 96 students without a GRE score, 432 students were reviewed for the presence of outliers. ROUT detected 39 outliers that were removed before statistical analysis was performed. Sample See detailed description in the Participants section. Linear regression analysis was used to examine potential trends between GRE scores, GRE percentiles, normalized admissions scores or GPA and outcomes between selected student groups. The D’Agostino & Pearson omnibus and Shapiro-Wilk normality tests were used to test for normality regarding outcomes in the sample. The Pearson correlation coefficient was calculated to determine the relationship between GRE scores, GRE percentiles, admissions scores or GPA (undergraduate and graduate) and time to degree. Candidacy exam results were divided into students who either passed or failed the exam. A Mann-Whitney test was then used to test for statistically significant differences between mean GRE scores, percentiles, and undergraduate GPA and candidacy exam results. Other variables were also observed such as gender, race, ethnicity, and citizenship status within the samples. Predictive Metrics. The input variables used in this study were GPA and scores and percentiles of applicants on both the Quantitative and Verbal Reasoning GRE sections. GRE scores and percentiles were examined to normalize variances that could occur between tests. Performance Metrics. The output variables used in the statistical analyses of each data set were either the amount of time it took for each student to earn their doctoral degree, or the student’s candidacy examination result.

This dataset contains organized student academic and demographic records, suitable for various educational data analysis and machine learning projects. It includes details such as student IDs, names, grades, class information, and other attributes that can be used for performance tracking, visualization, and predictive modeling.

Researchers, educators, and data enthusiasts can use this dataset to explore patterns in student performance, identify factors influencing learning outcomes, or build models for grade prediction and student profiling.

Whether you’re practicing data cleaning, creating visual dashboards, or training classification models, this dataset provides a clear and structured foundation for your work.

Japan No. of International Students: Myanmar data was reported at 4,816.000 Person in 2017. This records an increase from the previous number of 3,851.000 Person for 2016. Japan No. of International Students: Myanmar data is updated yearly, averaging 1,012.000 Person from Apr 2001 (Median) to 2017, with 17 observations. The data reached an all-time high of 4,816.000 Person in 2017 and a record low of 342.000 Person in 2001. Japan No. of International Students: Myanmar data remains active status in CEIC and is reported by Japan Student Services Organization. The data is categorized under Global Database’s Japan – Table JP.G009: Survey on Internation Students: Number of International Students in Japan.

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The dataset contains Academic-year-wise historically compiled data on total Number and Percentage of International Students in the United States of America (U.S.A).

This dataset contains student achievement data for two Portuguese high schools. The data was collected using school reports and questionnaires, and includes student grades, demographics, social, parent, and school-related features.

Two datasets are provided regarding performance in two distinct subjects: Mathematics and Portuguese language. I have cleaned the original datasets so that they are easier to read and use.

Attributes for both student_math_cleaned.csv (Math course) and student_portuguese_cleaned.csv (Portuguese language course) datasets:

1. school - student's school (binary: "GP" - Gabriel Pereira or "MS" - Mousinho da Silveira)
2. sex - student's sex (binary: "F" - female or "M" - male)
3. age - student's age (numeric: from 15 to 22)
4. address_type - student's home address type (binary: "Urban" or "Rural")
5. family_size - family size (binary: "Less or equal to 3" or "Greater than 3")
6. parent_status - parent's cohabitation status (binary: "Living together" or "Apart")
7. mother_education - mother's education (ordinal: "none", "primary education (4th grade)", "5th to 9th grade", "secondary education" or "higher education")
8. father_education - father's education (ordinal: "none", "primary education (4th grade)", "5th to 9th grade", "secondary education" or "higher education")
9. mother_job - mother's job (nominal: "teacher", "health" care related, civil "services" (e.g. administrative or police), "at_home" or "other")
10. father_job - father's job (nominal: "teacher", "health" care related, civil "services" (e.g. administrative or police), "at_home" or "other")
11. reason - reason to choose this school (nominal: close to "home", school "reputation", "course" preference or "other")
12. guardian - student's guardian (nominal: "mother", "father" or "other")
13. travel_time - home to school travel time (ordinal: "<15 min.", "15 to 30 min.", "30 min. to 1 hour", or 4 - ">1 hour")
14. study_time - weekly study time (ordinal: 1 - "<2 hours", "2 to 5 hours", "5 to 10 hours", or ">10 hours")
15. class_failures - number of past class failures (numeric: n if 1<=n<3, else 4)
16. school_support - extra educational support (binary: yes or no)
17. family_support - family educational support (binary: yes or no)
18. extra_paid_classes - extra paid classes within the course subject (Math or Portuguese) (binary: yes or no)
19. activities - extra-curricular activities (binary: yes or no)
20. nursery - attended nursery school (binary: yes or no)
21. higher_ed - wants to take higher education (binary: yes or no)
22. internet - Internet access at home (binary: yes or no)
23. romantic_relationship - with a romantic relationship (binary: yes or no)
24. family_relationship - quality of family relationships (numeric: from 1 - very bad to 5 - excellent)
25. free_time - free time after school (numeric: from 1 - very low to 5 - very high)
26. social - going out with friends (numeric: from 1 - very low to 5 - very high)
27. weekday_alcohol - workday alcohol consumption (numeric: from 1 - very low to 5 - very high)
28. weekend_alcohol - weekend alcohol consumption (numeric: from 1 - very low to 5 - very high)
29. health - current health status (numeric: from 1 - very bad to 5 - very good)
30. absences - number of school absences (numeric: from 0 to 93)

These grades are related with the course subject, Math or Portuguese:

1. grade_1 - first period grade (numeric: from 0 to 20)
2. grade_2 - second period grade (numeric: from 0 to 20)
3. final_grade - final grade (numeric: from 0 to 20, output target)

Important note: the target attribute final_grade has a strong correlation with attributes grade_2 and grade_1. This occurs because final_grade is the final year grade (issued at the 3rd period), while grade_1 and grade_2 correspond to the 1st and 2nd period grades. It is more difficult to predict final_grade without grade_2 and grade_1, but these predictions will be much more useful.

Additional note: there are 382 students that belong to both datasets, though the ID's do not match. These students can be identified by searching for identical attributes that characterize each student.

Please include this citation if you plan to use this database: P. Cortez and A. Silva. Using Data Mining to Predict Secondary School Student Performance. In A. Brito and J. Teixeira Eds., Proceedings of 5th FUture BUsiness TEChnology Conference (FUBUTEC 2008) pp. 5-12, Porto, Portugal, April, 2008, EUROSIS, ISBN 978-9077381-39-7.

Student engagement and learning data (as an anonymised CSV file).

2018 DC School Report Card. STAR Framework student group scores by school and school framework. The STAR Framework measures performance for 10 different student groups with a minimum n size of 10 or more students at the school. The student groups are All Students, Students with Disabilities, Student who are At Risk, English Learners, and students who identify as the following ESSA-defined racial/ethnic groups: American Indian or Alaskan Native, Asian, Black or African American, Hispanic/Latino of any race, Native Hawaiian or Other Pacific Islander, White, and Two or more races. The Alternative School Framework includes an eleventh student group, At-Risk Students with Disabilities.Some students are included in the school- and LEA-level aggregations that will display on the DC School Report Card but are not included in calculations for the STAR Framework. These students are included in the “All Report Card Students” student group to distinguish from the “All Students” group used for the STAR Framework.Supplemental:Metric scores are not reported for n-sizes less than 10; metrics that have an n-size less than 10 are not included in calculation of STAR scores and ratings.At the state level, teacher data is reported on the DC School Report Card for all schools, high-poverty schools, and low-poverty schools. The definition for high-poverty and low-poverty schools is included in DC's ESSA State Plan. At the school level, teacher data is reported for the entire school, and at the LEA-level, teacher data is reported for all schools only.On the STAR Framework, 203 schools received STAR scores and ratings based on data from the 2017-18 school year. Of those 203 schools, 2 schools closed after the completion of the 2017-18 school year (Excel Academy PCS and Washington Mathematics Science Technology PCHS). Because those two schools closed, they do not receive a School Report Card and report card metrics were not calculated for those schools.Schools with non-traditional grade configurations may be assigned multiple school frameworks as part of the STAR Framework. For example, a K-8 school would be assigned the Elementary School Framework and the Middle School Framework. Because a school may have multiple school frameworks, the total number of school framework scores across the city will be greater than the total number of schools that received a STAR score and rating.Detailed information about the metrics and calculations for the DC School Report Card and STAR Framework can be found in the 2018 DC School Report Card and STAR Framework Technical Guide (https://osse.dc.gov/publication/2018-dc-school-report-card-and-star-framework-technical-guide).

The University of Washington - Beyond High School (UW-BHS) project surveyed students in Washington State to examine factors impacting educational attainment and the transition to adulthood among high school seniors. The project began in 1999 in an effort to assess the impact of I-200 (the referendum that ended Affirmative Action) on minority enrollment in higher education in Washington. The research objectives of the project were: (1) to describe and explain differences in the transition from high school to college by race and ethnicity, socioeconomic origins, and other characteristics, (2) to evaluate the impact of the Washington State Achievers Program, and (3) to explore the implications of multiple race and ethnic identities. Following a successful pilot survey in the spring of 2000, the project eventually included baseline and one-year follow-up surveys (conducted in 2002, 2003, 2004, and 2005) of almost 10,000 high school seniors in five cohorts across several Washington school districts. The high school senior surveys included questions that explored students' educational aspirations and future career plans, as well as questions on family background, home life, perceptions of school and home environments, self-esteem, and participation in school related and non-school related activities. To supplement the 2000, 2002, and 2003 student surveys, parents of high school seniors were also queried to determine their expectations and aspirations for their child's education, as well as their own educational backgrounds and fields of employment. Parents were also asked to report any financial measures undertaken to prepare for their child's continued education, and whether the household received any form of financial assistance. In 2010, a ten-year follow-up with the 2000 senior cohort was conducted to assess educational, career, and familial outcomes. The ten year follow-up surveys collected information on educational attainment, early employment experiences, family and partnership, civic engagement, and health status. The baseline, parent, and follow-up surveys also collected detailed demographic information, including age, sex, ethnicity, language, religion, education level, employment, income, marital status, and parental status.

United Kingdom UK: Over-Age Students: Primary: % of Enrollment data was reported at 1.126 % in 2015. This records an increase from the previous number of 1.067 % for 2014. United Kingdom UK: Over-Age Students: Primary: % of Enrollment data is updated yearly, averaging 1.594 % from Dec 1971 (Median) to 2015, with 31 observations. The data reached an all-time high of 7.386 % in 1979 and a record low of 0.000 % in 2003. United Kingdom UK: Over-Age Students: Primary: % of Enrollment data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United Kingdom – Table UK.World Bank.WDI: Education Statistics. Over-age students are the percentage of those enrolled who are older than the official school-age range for primary education.; ; UNESCO Institute for Statistics; ;

The dataset contains Academic-year- and Country-wise historically compiled data on the total number of International students enrolled for studying Undergraduate, Graduate, Non-Degree and Optional Practical Training (OPT) courses in the United States of America (USA).

Publicly accessible databases often impose query limits or require registration. Even when I maintain public and limit-free APIs, I never wanted to host a public database because I tend to think that the connection strings are a problem for the user.

I’ve decided to host different light/medium size by using PostgreSQL, MySQL and SQL Server backends (in strict descending order of preference!).

Why 3 database backends? I think there are a ton of small edge cases when moving between DB back ends and so testing lots with live databases is quite valuable. With this resource you can benchmark speed, compression, and DDL types.

Please send me a tweet if you need the connection strings for your lectures or workshops. My Twitter username is @pachamaltese. See the SQL dumps on each section to have the data locally.

The dataset provides a collection of 160 instances belonging to two classes (pass' = 136 andfail' = 24). The data is an anonymised, statistically sound and reliable representation of the original data collected from students studying computer science modules at a UK University. Each instance is made up of 19 features plus the class label. Eight of the features represent students' online behaviour including bio information retrieved from Virtual Learning Environment. Eleven of the features represent students' neighbourhood influence retrieved from Office for Students database. The data has been compiled and made available in de-facto/de-jure standard open formats (CSV and JSON).

This data was collected and used in a research study undertaken by academics and researchers at Computer Science Department, Edge Hill University, United Kingdom. To encourage reproducibility of the experiments and results reported, the data is provided in the exact training-validation-testing splits used in the experiments.